More efficient search algorithm (MESA) using virtual search parameters

ABSTRACT

A more efficient search algorithm introduces a variety of new tools and strategies to more efficiently search and retrieve desired records from an electronic database. Among these are a strategy that utilizes the first and last positional characters, or phonemes, to exploit the fact that often last positional character is nearly as important as a first positional character in distinguishing database records from one another. In addition, virtual search parameters, that are not a portion of the database records, can also be utilized in distinguishing database records, such as by identifying a number of characters in a search field for a requested database record as a way of distinguishing that record from all others with a different number of characters. The invention finds potential application in any database search application, but is particularly useful in delivering directory assistance services.

RELATION TO OTHER PATENT APPLICATIONS

This application claims the benefit of provisional application No.60/607,680, filed Sep. 7, 2004, entitled more efficient search algorithm(MESA); and provisional application 60/618,755, filed Oct. 14, 2004 withthe same title; and provisional application 60/684,728, filed May 26,2005, again with the same title.

TECHNICAL FIELD

This invention relates generally to systems and methods for selectivelyidentifying, retrieving and manipulating electronically storedinformation, and more specifically, to systems and methods forselectively identifying, retrieving and manipulating desired orrequested information from a directory assistance search environment.

BACKGROUND ART

Data Retrieval, Generally

Current data retrieval methods rely on identifying and retrieving one ormore data records from an informational database by executing some formof sequence-based, alphanumeric search query for one or more particulardata fields. In order to effectively identify a desired record, asufficient number of forward-sequential, alphanumeric characters must beentered into a search query to perform an exact match and correspondingproper identification and retrieval of a desired data record. Typically,an exact match of an entire whole alphanumeric word or data field isrequired to ensure one hundred percent confidence that the retrievedrecord is, in fact, the desired record. An example of a successful,whole-word, forward-sequential search query would require a user toenter “JOHN” in a first name field and “SMITH” in a last name field tofind the database record for JOHN SMITH in a database containing names.

Current art has improved somewhat upon the need for an exact, whole-wordor field match by allowing data retrieval systems to retrieve recordswith only a partial, forward-sequential, alphanumeric match of one ormore data fields. Using the JOHN SMITH example above, a user may type inone or more forward-sequential letters of the first name, such as “JO”and one or more forward-sequential letters of the last name, such as“SM” into a search query. As with the whole-word match query, all JOHNSMITH data records are retrieved. One particular advantage of this typeof partial, forward-sequential query is to minimize the number ofkeystrokes a user may be required to enter when retrieving data records.A significant disadvantage of this type of partial, forward-sequentialquery is that all data records that begin with “JO” in the first namefield and begin with “SM” in the last name field are retrieved. The datarecord for JOSEPH SMALL is just as likely to be retrieved as the recordfor JOHN SMITH. The confidence of correct record identification andretrieval can only be increased as more and more forward-sequentialcharacters are added to the appropriate search fields. In addition tomethods for formulating and executing a single search query, prior artalso teaches methods and systems for sequencing or ordering multiplesearch queries. Current art teaches that the middle, or interveningcharacters between the first and last positional characters are integralto both an exact, whole-word matching process and to an increasingconfidence of an exact, forward-sequential, partial-word matchingprocess.

Directory Assistance

Today's Directory Assistance (DA) retrieval systems and architecturealso follow a strict, forward-sequential, partial-word matching processin which only the first three characters of a search term are used toretrieve all listing that match these first three characters. This basictrigram format is relatively inefficient in that it retrieves numerouslistings which have a great likelihood of having the same, or verysimilar, spellings. A directory assistance agent may spend considerabletime and resources paging through screens of retrieved, matching recordsin an attempt to identify the correct requested listing. Additionally,current DA database architecture is well-defined and usually containsthree fixed search fields, each with a fixed length of 12 characters.Separate databases are also used for business, Most Frequently Requested(‘MFR’), Residence and Federal, State and local governments. Today'sbasic architecture has remained essentially the same since themid-1970s. As then, the theory and intuitive and seemingly mostefficient approach was that the first character of the primary namesearch field was the most important search character, with the additionsof the second and third sequence-based search characters beingincrementally relevant in a diminishing manner. Additional charactersafter the initial three characters were considered completelyineffective for common trigrams like INDiana and INDianapolis.

The recent use of automatic voice recognition (‘AVR’) technology hasachieved a limited increase in efficiency and productivity. However,speaker independent systems and the requirement to correctly interpretor translate the request from over 200 million customers and then searchand retrieve the one exact match out of 200 million directory listingsis extraordinarily difficult. The result is a very low percentage ofcalls ‘contained’ within the AVR system and a correspondingly highsubstitution or error rate. Successful AVR calls are typically limitedto high volume, MFR business directory requests with virtually noresidential requests being completed with speaker independent AVRtechnology.

Despite the incremental improvements in productivity made by AVRtechnology, a number of problems still exist. These problems fall underseveral broad categories. For example, powerful search characters withinthe search parameters have not been included in the search algorithms;agent workstation consoles have not been integrated with databasearchitecture, search parameters or search algorithms; unique databasecharacteristics and structure have not been recognized; the rootproblems have not been clearly identified, AVR capability has not beenfully integrated into the total process, and the agents Subject MatterExpertise (‘SME’) of the database has not been utilized.

The present invention is directed to improving these and other aspectsof informational database record identification and retrieval methodsand systems.

DISCLOSURE OF THE INVENTION

In a first preferred embodiment of the invention, a novel databasesearch query method and system that utilizes only the first and lastpositional characters of one or more search fields is executed upon oneor more informational databases. At a minimum, each informationaldatabase contains at least one data field that is capable of beingqueried on the first and last positional characters of data recordscontained within that data field. Additionally, each database maycontain at least one data field that is capable of being queried on anyone or more additional key characters positioned anywhere between thefirst and last positional characters of data records contained withinthat data field. Database queries may be executed via a keyed-inputmethod and system as well as a voiced-input method and system utilizingcurrent AVR technology.

In a preferred aspect of this embodiment, the invention is additionallycapable of determining, for each informational database, the statisticalfrequency distribution (frequency of occurrence (%)) of each and everyunique first and last positional character combination for every datarecord within one or more searchable data fields. This frequency ofoccurrence information may be determined in a predetermined or dynamicmanner. The frequency of occurrence information is further utilized tohierarchically arrange and execute multiple search queries in order of amost-restrictive to a least-restrictive query. Moreover, the inventionis capable of determining and utilizing the statistical frequencydistribution information for all records within a database as well asfor any subset of records, including those records retrieved by a priorsearch.

In another preferred aspect, the invention is capable of determining andutilizing Virtual Search Parameters (‘VSPs’) in search queries executedupon one or more informational databases. A VSP is a search parameterthat is derived from an objective or subjective attribute of recordscontained within one or more data fields. The primary objects of a VSPinclude: more efficiently eliminating similar, but incorrect datarecords; and more efficiently increasing the probability of identifyinga correct listing.

VSPs may be based on either an objective or subjective attribute ofrecords within a single or plurality of data fields. Objectiveattributes include those attributes that are quantifiable or physicaland are considered ‘universally accepted’ facts. As such, objective VSPsare generally ‘true’ for each user of the system. Subjective attributesare those attributes that are generally qualitative in nature and may bebased on any personal or non-objective attribute or characteristic. Assuch, subjective attributes are not necessarily ‘true’, or the same, foreach user of the system.

VSPs may be derived from objective or subjective attributes of a singlerecord or a group of records, contained within a single data field or agroup of data fields, belonging to a single informational database ormultiple, related databases. Examples of an objective VSP includestatistically-based characteristics of a particular informationaldatabase, such as the actual frequency of occurrence for a particularfirst and last positional character combination. A correspondingsubjective VSP includes categorizations based on the actual frequency ofoccurrence, such as common and uncommon.

In another preferred aspect, a novel method and system for retrievingrecords from an informational database utilizing current AVR technologyis taught. The method consists of initially receiving vocalized input ofboth the first and last positional characters for one or more searchfields. The vocalized input may consist of either the first and lastpositional characters, where each desired character is voicedindividually, or the first and last positional phonemes, where eachphoneme is voiced individually. Alternatively, the desired first andlast positional characters or phonemes of a particular search field maybe isolated from the vocalized input of an entire search term. Undereither approach, i.e. whether the first and last positional charactersor phonemes are input individually through voiced utterances, or whetherthe first and last positional characters or phonemes are isolated fromwhole-word search terms input through voiced utterances, the methodutilizes these first and last positional characters or phonemes toexecute a database search. This database search may be executed in oneof several alternative methods. Each method relies on a novelapplication of current AVR technology.

A first preferred method of executing a voiced-input search includesspeech-to-text translation of the voiced input, including speech-to-texttranslation of characters or speech-to-text translation of phonemes. Asis taught in current art, every phoneme may be represented by somecombination of alphabetic letters. Under the speech-to-text translationmethod utilizing characters, and not phonemes, the search query isexecuted upon the database in the same manner as a keyed-input searchquery. Under the speech-to-text translation method utilizing phonemes,the database must be structured or formatted in such a way as to allowfor searching on the alphabetic text equivalents of the first and lastpositional phonemes. Specifically, the database may contain thealphabetic text equivalents for at least the first and last positionalphonemes for each data record contained within one or more searchabledata fields. These alphabetic text equivalents for the first and lastpositional phonemes may be located, and accessed and may bepredetermined or dynamic. If dynamic, then the alphabetic textequivalents for the relevant phonemes may be derived or calculated usingany method of programming available, as is practiced today in therelevant art.

A second preferred method of executing a voiced input search utilizescurrent voiceprint or phoneme audiogram recognition and matchingtechnology to first interpret the voiced first and last positionalphonemes. Once interpreted, the method next retrieves those databaserecords where the audiograms or voiceprints of the interpreted first andlast positional phonemes of the search query match the audiograms orvoiceprints of the first and last positional phonemes of the particulardata records.

In a second preferred embodiment of the invention, the novel searchsystem and method of the first preferred embodiment is performed withina directory assistance call center environment, preferably utilizing alive call handling agent and both speaker-independent andspeaker-dependent AVR technology.

In one preferred aspect of this second preferred embodiment, a live callhandling agent utilizes a personal phoneme database to more efficientlyand more effectively formulate and execute search queries in accordancewith the system and method of the first preferred embodiment.

In a second preferred aspect of this second preferred embodiment, acomposite progressive confidence score that is calculated from aplurality of sources, including all components of the AVR method andsystem as well as from all components of the novel search system andmethod. In particular the statistical characteristics inherent to aparticular database, are utilized to more effectively and moreefficiently to formulate and execute search queries in accordance withthe first preferred embodiment of the invention.

In a third preferred aspect of this second preferred embodiment, asystem and method that utilizes an integrated system optimizer and anintelligent query system to dynamically assist a call handling agent orautomated attendant is utilized to formulate and execute more effectiveand more efficient search strategies in accordance with the firstpreferred embodiment of this invention.

In a fourth preferred aspect of this embodiment, agent operand commandsare utilized to direct a specific computer program instruction or step.These agent operand commands are composed of an action, a linking, andan object word. The combination of action, linking and object commandwords allows for the possibility of formulating multiple database searchqueries. Action words include “search”, “expand”, “delete.” Linkingwords include Boolean operators, specifically including “And”, “or”,“all”, “except”, “only”, “not.” Object words include an element of adatabase record field, including any positional element of the field;object includes virtual search parameters, including the virtual searchparameter categorizations of ‘common’ and ‘uncommon.’

In a fifth preferred aspect of this invention, an integrated agentworkstation is utilized to more effectively and efficiently formulateand execute search queries using the Alpha-Omega search methodology ofthe first preferred embodiment. The understanding of the insights intothe Alpha-Omega search method and unique database characteristics leadto the fundamental need for an integration of the agent's work tool, theagent's workstation, with database architecture, search algorithms,search parameters and AVR technology.

It is one object of the present invention to provide a system and methodfor selectively identifying and retrieving data records from aninformational database that does not rely on correct spelling orwhole-word matching.

It is another object of the present invention to provide a system andmethod for selectively identifying and retrieving data records from aninformational database that utilizes only the first and last positionalcharacters or phonemes of one or more search fields.

It is yet another object of the present invention to provide a systemand method for selectively identifying and retrieving data records froman informational database that utilizes the statistical characteristicsinherent to a particular database or any subset of the database toselectively identify, retrieve, or manipulate data records or datarecord search queries.

It is still another object of the present invention to provide a systemand method for utilizing virtual search parameters that are derived froman objective or subjective attribute of a data records or data fieldscontained within one or more particular databases, where these virtualsearch parameters more effectively and more efficiently increase theprobability of identifying, retrieving and manipulating desired datarecords.

It is another object of the present invention to provide a system andmethod for utilizing both speaker-independent and speaker-dependentvoice recognition technology in conjunction with the novel search systemand method to more effectively and more efficiently selectivelyidentify, retrieve and manipulate desired data records.

It is yet another object of the present invention to provide a systemand method for utilizing a progressive composite confidence score thatis calculated from a plurality of sources, including all components ofthe AVR method and system as well as from all components of the novelsearch system and method, in particular the statistical characteristicsinherent to a particular database.

It is yet another object of the present invention to provide a systemand method for utilizing an integrated system optimizer (ISO) and anintelligent query system (IQS) to dynamically assist a call handlingagent or automated attendant formulate and execute more effective andmore efficient search strategies.

It is yet another object of the present invention to utilize agentoperand commands and an integrated agent workstation in order to moreeffectively and efficiently formulate and execute search methodologies.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram of a representational standard computer systemcapable of employing the principles of the invention.

FIG. 2 is a drawing depicting the interrelationship among theAlpha-Omega Search Module (AOSM), the More Effective Search Sequence(MESS) module, and an informational database.

FIG. 3 is a drawing depicting the functional components of theAlpha-Omega Search Module.

FIG. 4 depicts a diagram of the key components capable of performing theMESS functions.

FIG. 5 is a flowchart showing a preferred functionality of the MESSmethod in which multiple search queries are ordered and executedaccording to a preferred MESS analysis.

FIG. 6 is a flowchart depicting a search methodology utilizing virtualsearch parameters

FIG. 7 is a representational diagram showing the components of adirectory assistance call environment utilizing the method and system ofthe present invention.

DETAILED DISCLOSURE

The present invention improves upon current data retrieval systems andmethods in a number of novel ways. Specifically, the present inventionrelies upon the following improvements, among others: a searchmethodology that does not rely on exact, whole-word matching; a searchmethodology that does not rely on sequential, forward-fill matching; asearch methodology that does not rely on trigram or other sequence-basedletter combinations matching; a search methodology that does not rely onintervening characters between the first and last positional charactersof any search term; a search methodology that does not rely on correctspelling; a search methodology that uses only the first and lastpositional characters or phonemes of one or more particular search termsto uniquely identify and retrieve records; and finally, a searchmethodology that does rely on the statistical characteristics inherentto a particular database to more efficiently identify and retrieverecords.

In a first preferred embodiment of the invention, a system and method istaught for generalized data retrieval from an informational database.The system and method executes a novel database search query thatutilizes only and both the first and last positional characters of oneor more search fields upon one or more informational databases.

As illustrated in FIG. 1, there is shown a diagram of a representationalstandard computer system capable of employing the principles of theinvention. This computer system may be any type of computer systemcapable of storing and retrieving records from an informationaldatabase. Generally, the computer-based database retrieval system shouldinclude a computer, with a central processing unit; a memory device; astorage device; and a display screen/console.

Additionally, the system utilizes preferably a user of the computersystem, where the user functions as the requestor of a desired record,where such requestor may be a live or automated or computer-basedrequestor, an informational database containing any type of searchableor indexable data records, and a means for executing one or moredatabase search queries. The system includes any type of input means,including: a keyed-input means; a voiced-utterance means, eitherspeaker-independent or speaker dependent; a computer-generated orautomated (programmed) means requiring no contemporaneous human input;or any other input means by which a search query could be executed uponthe database. Preferably the system includes a workstation with akeyboard, a display monitor, and an interactive voice recognition unit.The system may also include some type of telephonic communicationsdevice for accessing and searching databases that are not directlylinked to the requestor's workstation.

Referring now to FIG. 2, there is shown a diagram of the key functionalcomponents of the first preferred embodiment of the present invention,including the Alpha-Omega Search Module (‘AOSM’), an Architecture,Structure, Organization and Format (‘ASOF’) informational database; anda More Effective Search Sequence Module (‘MESS’). The primary functionof the AOSM is to accept query input, where the query input ispreferably comprised of both and only the first and last positionalcharacters of each search term for one or more search parameters. Forpurposes of this disclosure, a search term is intended to refer to theword(s) or character(s) that are input to a particular search query. Asearch parameter is intended to refer to the entire data field uponwhich the search query is executed. A search query is comprised of oneor multiple search parameter fields into which a user may input avariety of search terms. As an example, in searching for the name ‘JohnSmith’ in an informational database containing, at a minimum, one datafield defined as First Name and a second data field defined as LastName, the First Name and Last Name data fields are intended to be calledSearch Parameters, John and Smith are intended to be called SearchTerms. Additionally, the first positional character of any term withinany data field of any record will be referred to as the “Alpha”character; correspondingly, the last positional character will bereferred to as the “Omega” character. The combination of first and lastpositional characters will be referred to as the “Alpha-Omega”combination and may be abbreviated as the A-O combination.

Referring now to FIG. 3, the AOSM is comprised of the followingcomponents: a Query Input component (‘QI’); a Query Execution component(‘QE’); a Potential Record Pool component (‘PRP’); and a RecordDisposition component (‘RD’). In this first preferred embodiment, theAOSM queries and retrieves records from one or more databases, whereeach database preferably is in an Architecture, Structure andOrganization Format (‘ASOF’), described more fully below. The inventionidentifies and retrieves a data record from an informational database byexecuting a search query in which only and both the Alpha and Omegapositional characters of records contained in one or more searchabledata fields within one or more databases are matched.

In a first preferred step of this method, the QI component of the AOSMaccepts user input of the Alpha and Omega positional characters of oneor more search terms. The QE component receives the query input andexecutes the search query upon the appropriate search parameter fieldsin the informational database. The QE further includes a means forexecuting searches based on any positional search characters of asearchable parameter field. A searchable parameter field is a databasefield capable of being queried, at a minimum, on the Alpha and Omegapositional characters for records contained within that field. The QEcomponent also includes a means for identifying a data record bymatching or not matching a search query term.

In a second preferred step of this method, the QE component retrievesall records where the Alpha and Omega positional characters of theparticular record match the Alpha and Omega positional characters of thesearch query terms. The retrieved records are submitted to the PRPcomponent. The PRP is a sub-component of the AOSM and the primaryfunctionality of the PRP component is to accept data records retrievedfrom the QE component; order or rank said retrieved records, acceptinput from MESS and other input sources; display said records and otherinput from various sources; and submit records for further processing,to the RD component, or to some other function.

The invention also requires that the AOSM execute the novel search querymethod upon one or more informational databases. Each database ispreferably in the Architecture, Structure, and Organization Format. TheASOF database contains, at a minimum, at least one data field that iscapable of being queried on the Alpha and Omega positional characters ofdata records contained within that data field. Additionally, eachdatabase may contain at least one data field that is capable of beingqueried on any one or more additional key characters positioned anywherebetween the Alpha and Omega positional characters of data recordscontained within that data field. Preferably, the database is capable ofbeing queried on each and every positional character for all recordscontained within one or more data fields. Database queries may beexecuted via a keyed-input method and system as well as a voiced-inputmethod and system utilizing current AVR technology.

In a third preferred step of this method, the PRP submits the retrievedset of records from the initial query to the user for further querying,in the event that more than one record is retrieved. The PRP componentinteracts with the QI component and the QE component to accept andexecute a second search query on the retrieved subset of records. Aswith the initial query, the user inputs the Alpha and Omega positionalcharacters of the search term desired for a particular search parameterfield. The PRP component receives this second set of retrieved recordsfrom the second query. In the event that more than one record isretrieved, the PRP will again interact with both the QI component and QEcomponent to accept and execute a third search query on this secondretrieved subset of records. This process is capable of continuing inthis manner until the single desired or requested record(s) is located.Once the single desired record(s) is located, the PRP submits therecord(s) to the RD component for further action based on the user'sneeds.

The unique and novel aspect of this particular search methodologyutilizing only the Alpha and Omega positional characters is theunderstanding that the last alphabetic character in any search field isas powerful as the first character. In fact, it can be demonstrated thatmultiple parameter searches in which only the Alpha and Omega positionalcharacters are used in search terms can be more effective thanmulti-character, sequential searches using only a single parameter. Theunderlying rationale is rooted in the inherent structure of analphabetized database. When a typical forward-sequential, first-to-lastcharacter search is executed, a multitude of very similar listings isretrieved, with virtually little opportunity to differentiate, at anearly stage of the search query sequence, the various records. As anexample, in an informational database containing names and address ofbusinesses in Indiana, the search query for the ‘Indianapolis AthleticClub’ located on ‘Meridian Street’, the typical forward sequentialsearch would begin with the user inputting the letters “IND” under acommon trigram approach. In this case, a multitude of records that matchthis query are retrieved, including, among others, all business listingsthat have Indiana, Indianapolis, or Industrial in their title. Incontrast, when the methodology of the present invention is used, theinitial search query would be “I . . . S”, thereby eliminating allIndiana and all Industrial records. Understandably, other records thatbegin with I and end with S may also be retrieved. The methodology ofthis invention, however, is based, in part, on the understanding thatmultiple search parameters in which only the Alpha and Omega positionalcharacters of search terms are used is more effective and efficient thanusing many characters in a forward sequential search with one parameter.In the example above for the search for ‘Indianapolis Athletic Club’, aninitial search using ‘I_S’ for the business name and ‘M_N’ for thestreet name retrieves only a single record in the Indianapolis BusinessDirectory. Additionally, new intra-field sub-search algorithms mayeliminate 90 to 98 percent of most listing pools. They becomeparticularly effective when linked to the existing trigram searchalgorithm and multiple search fields.

The Alpha and Omega positional characters in a search term are uniqueidentifiers; in fact, these two positional characters are the mostpowerful identifiers and the combination of the two characters isespecially powerful in retrieving desired records. It is important tonote, however, that a primary object of this specific novel searchmethodology is to more efficiently retrieve a desired record byeliminating similar, yet incorrect records more efficiently. Theinvention utilizes several approaches to achieve this increasedefficiency. As noted above, a single search query utilizing only andboth the Alpha and Omega positional character combination for a singlesearch term eliminates similarly spelled, yet incorrect records, andresults in a PRP of dissimilar and less ambiguous records. The additionof a second search query utilizing only and both the Alpha and Omegapositional character combinations for an additional search term not onlyfurther eliminates incorrect records, but significantly increases theprobability of retrieving a desired record due to the highly uniquecombination of two very powerful Alpha and Omega positional charactersearches. Not only will a multiple parameter, Alpha and Omega positionalcharacter-only query significantly increase the probability ofretrieving a desired record, but the search methodology of thisinvention further provides for a novel sequencing or arranging of searchqueries in order to increase the search efficiency even more.

In a preferred aspect of this embodiment, the invention is additionallycapable of determining, for each informational database, the statisticalfrequency distribution (frequency of occurrence (%)) of each and everyunique first and last positional character combination for every datarecord within one or more searchable data fields. This frequency ofoccurrence information may be determined in a predetermined or dynamicmanner. The frequency of occurrence information is further utilized tohierarchically arrange and execute multiple search queries in order of amost-restrictive to a least-restrictive query. Moreover, the inventionis capable of determining and utilizing the statistical frequencydistribution information for all records within a database as well asfor any subset of records, including those records retrieved by a priorsearch.

Referring now to FIG. 4, a diagram of the key components capable ofperforming the functions described in this preferred aspect is shown.The More Efficient Search Sequence (‘MESS’) module is comprised, at aminimum, of three primary components: an Input Component; an AnalysisComponent; and an Output Component. The Input Component is comprised ofmultiple input means, including, but not limited to an input means forthe AOSM, the ISO and IQS, described more fully, below, and any otherinput means, as required. The Analysis Component is comprised, at aminimum, of a statistical computation means and a database lookup andanalysis means. The Output Component is comprised, at a minimum, of adisplay means, a reporting means and any other means, as required. Allthree of these components of the MESS are capable of exchanginginformation among one another as required. In addition, the AnalysisComponent is capable of interacting with any informational database byutilizing the database lookup and analysis means.

FIG. 5 is a flowchart showing a preferred functionality of the MESSmethod in which multiple search queries are ordered and executedaccording to a preferred MESS analysis. A preferred order of queryexecution is from a most-restrictive to a least-restrictive query.Preferably MESS orders queries based on the frequency of occurrence ofthe A-O combinations for multiple search terms. A most-restrictive queryis the query that generates the fewest number of matching records, i.e.the A-O combination of a search term with the lowest percent occurrencein the database. Preferably, the resulting set of retrieved recordsmatching this initial query are returned in the form of a PRP, eitherdisplayed to the user for further querying input or held in cache memoryas a PRP for further analysis. In any event, this resulting PRP is usedas the basis for the frequency of occurrence calculations for theremaining search terms. Once re-calculated, the search term with thelowest percent frequency of occurrence within this initial PRP isexecuted. Again, a new, second PRP is created, PRP2, and the frequencyof occurrence for any remaining first and last positional search termsis re-calculated from PRP2 and the most-restrictive query is thenexecuted. This process continues in this fashion until the desiredrecord is located or a PRP of sufficient probability of identifying thedesired record is created.

The primary function of the MESS is to order search queries in ahierarchical manner and relies on the frequency distribution ofpositional characters within one or more particular search terms, wheresaid frequency distribution is determined through a statistical analysisof positional characters of data records within one or more data fieldswithin one or more ASOF databases. It is important to note that the MESSpreferably relies on the frequency distribution of the A-O combinationsfor data records within one or more data fields in order to work withthe AOSM. However, it is envisioned that the MESS will also function byrelying on the frequency distribution of any key positional character orcombination(s) of positional characters, including entire words orphrases included in one or more particular search fields. Moreover, MESSis capable of utilizing any type of statistical data derived from anycharacteristic of one or more particular databases. MESS utilizing theAOSM should be interpreted as only one method of uniquely identifyingand retrieving records by ordering and executing search queries based onstatistical frequency distribution of key positional characters. MESS iscapable of prioritizing and executing multiple parameter queries as wellas multiple, single parameter queries.

In another preferred aspect, the invention is capable of determining andutilizing Virtual Search Parameters (‘VSPs’) in search queries executedupon one or more informational databases. A VSP is a search parameterthat is derived from an objective or subjective attribute of recordscontained within one or more data fields. The primary objects of a VSPinclude: more efficiently eliminating similar, but incorrect datarecords; and more efficiently increasing the probability of identifyinga correct listing.

VSPs may be based on either an objective or subjective attribute ofrecords within a single or plurality of data fields. Objectiveattributes are those attributes that are quantifiable or physical andare considered ‘universally accepted’ facts. As such, objective VSPs aregenerally ‘true’ or ‘false’ for each record universally for each user ofthe system. Subjective attributes are those attributes that aregenerally qualitative and relative in nature and may be based on anypersonal or non-objective attribute or characteristic. As such,subjective attributes are not necessarily ‘true’, or the same, for eachuser of the system.

VSPs may be derived from objective or subjective attributes of a singlerecord or a group of records, contained within a single data field or agroup of data fields, belonging to a single informational database ormultiple, related databases. Examples of an objective VSP includestatistically-based characteristics of a particular informationaldatabase, such as the actual frequency of occurrence for a particularA-O combination. A corresponding subjective VSP includes categorizationsbased on the actual frequency of occurrence, such as common anduncommon.

The invention contemplates utilizing any number and variety of objectiveand subjective VSPs as a part of the search methodology. The inventioncontemplates, but is not limited to, utilizing the following objectiveand subjective VSPs as identified in the table below: AttributeObjective VSP Subjective VSP Length # of characters Short, Average, LongLanguage English, Greek, etc. Foreign Frequency Actual frequency of Verycommon, common, occurrence of positional uncommon, very uncommoncharacters or combinations of positional characters Location ActualCity, State, ‘City’, Suburban, Rural, etc. Business Medical,Professional, etc. Classifications Type Yellow Page Complexity Actual #of syllables; Difficult, Hard, Multiple Actual # of phonemes Syllables,Multiple Phonemes Purpose Fax, Police, etc Emergency, Data, etc. or Use

VSPs may be created in either a predetermined or dynamic manner.Preferably, the invention creates VSPs in both a predetermined anddynamic manner. Each possible VSP, whether objective or subjective, maybe further classified according to whether the VSP is an absolute VSP ora relative VSP. An absolute VSP is a VSP in which the specific attributeused to define the VSP remains constant for a particular record at alltimes. A relative VSP is a VSP in which the specific attribute used todefine the VSP may change for one or more records. An example of anabsolute VSP is the subjective attribute for location, such as ‘Rural.’It is anticipated that the designation of the subjective VSP forlocation of a particular record as ‘Rural’ would remain ‘Rural’ at alltimes. Examples of a relative VSP are both the objective and thesubjective VSPs for frequency of occurrence. The objective VSP forfrequency, i.e. percent occurrence for a particular A-O combination, maychange, depending upon the particular set of records that combination iscalculated from. For example, a percent occurrence for the A-Ocombination of 5%, if calculated from an entire database, is expected tobe different than the percent occurrence of that same A-O combination ifcalculated from a subset of database records, such as a PRP.Correspondingly, the subjective VSP for frequency, such as ‘common’ or‘uncommon’ would also change as the set of records used to derive thesubjective VSP classification changes. Preferably, absolute VSPs arecreated in a predetermined manner, while relative VSPs may be calculatedin a dynamic manner.

VSPs may be created using current database programming technology. Oneor more classification descriptors are created for the particular VSP.Preferably, each VSP has a one-to-one relationship with a particulardata field. Each classification descriptor has a unique set of rules,i.e. programming code, to follow in order to determine whichclassification descriptor should be assigned to which records.

The use of VSPs within the invention's search methodology is novel inthat a particular VSP is capable of more effectively and efficientlyincreasing the probability of successfully locating a desired record.The probability of successfully locating a desired record is achieved inone of several alternate, yet complementary ways. First, the VSP may beused to efficiently eliminate many similar but incorrect records. Anexample would be the use of the subjective VSP for frequency ofoccurrence of the A-O combinations. If the A-O combination for a searchtem is a highly unique combination of letters, i.e. whether thecombination of letters occurs with a very low frequency, within aparticular database, then a user of the system may execute a searchquery utilizing the subjective VSP for frequency of occurrence for A-Ocombinations to eliminate all records where their particular A-Ocombinations occur with a high frequency. In this case, the user wouldexecute a search query using a standard Boolean operator, “NOT”, inconjunction with the subjective VSP “Common” to eliminate all recordswhich have been categorized as “Common.” The net result is toeffectively select all records which have not been categorized as“Common” for further analysis. The resulting retrieved record set wouldcontain all those records categorized as “Uncommon” and “Very Uncommon,”among other categorizations, potentially. This resulting set of recordscontained within this first PRP contains the desired record, along withother records, with only a single search query having been executed.Moreover, the probability of retrieving the desired record is increasedrelative to a traditional search technique. This increased probabilityresults from several factors. First, the resulting PRP from a VSP-basedsearch query is considerably smaller than from a conventional searchmethodology. Moreover, the retrieved set of records in the PRP aredissimilar in spelling and contain a much more diverse and uniquecombination of records. In addition to eliminating incorrect recordsmore efficiently, a VSP can be utilized to select a subset of recordsbased on the frequency of occurrence. In this case, the VSP ‘VeryUncommon’ would retrieve a very small subset of records which match the‘Very Uncommon’ classification.

As described above, subjective VSPs, such as the frequency of occurrenceof A-O combinations are capable of being dynamically calculated. Assuch, the invention is capable of calculating the frequency ofoccurrence for A-O combinations for all records within any given subsetof database records and at any time. This novel feature may be utilizedat any time and for any purpose as required by the invention's searchmethodology.

In addition to the objective and subjective VSPs, described above, theinvention further presupposes a VSP based on a non-positional searchcharacter. The non-positional search character query entails a userinputting a single desired character for a record, where such characteris preferably located at any position within the intervening charactersbetween the first and last positional characters. This non-positionalsearch character VSP, as well as the objective and subjective VSPs, areespecially important to this invention's search methodology as itrelates to voiced-input queries using AVR technology, described morefully, below.

FIG. 6 depicts a flowchart of a search methodology utilizing virtualsearch parameters.

In another preferred aspect, a novel method and system for retrievingrecords from an informational database utilizing current AVR technologyis taught. The method consists of initially receiving vocalized input ofboth the first and last positional characters for one or more searchfields. The vocalized input may consist of either the first and lastpositional characters, where each desired character is voicedindividually, or the first and last positional phonemes, where eachphoneme is voiced individually. Alternatively, the desired first andlast positional characters or phonemes of a particular search field maybe isolated from the vocalized input of an entire search term. Undereither approach, i.e. whether the first and last positional charactersor phonemes are input individually through voiced utterances, or whetherthe first and last positional characters or phonemes are isolated fromwhole-word search terms input through voiced utterances, the methodutilizes these first and last positional characters or phonemes toexecute a database search. This database search may be executed in oneof several alternative methods. Each method relies on a novelapplication of current AVR technology.

A first preferred method of executing a voiced-input search includesspeech-to-text translation of the voiced input, including speech-to-texttranslation of characters or speech-to-text translation of phonemes. Asis taught in current art, every phoneme may be represented by somecombination of alphabetic letters. Under the speech-to-text translationmethod utilizing characters, and not phonemes, the search query isexecuted upon the database in the same manner as a keyed-input searchquery. Under the speech-to-text translation method utilizing phonemes,the database may be structured or formatted in such a way as to allowfor searching on the alphabetic text equivalents of the first and lastpositional phonemes. Specifically, the database may contain thealphabetic text equivalents for at least the first and last positionalphonemes for each data record contained within one or more searchabledata fields. These alphabetic text equivalents for the first and lastpositional phonemes may be located and accessed in any manner usingcurrent database programming methods and systems.

A second preferred method of executing a voiced input search utilizescurrent voiceprint or phoneme audiogram recognition and matchingtechnology to first interpret the voiced first and last positionalphonemes. Once interpreted, the method next retrieves those databaserecords where the audiograms or voiceprints of the interpreted first andlast positional phonemes of the search query match the audiograms orvoiceprints of the first and last positional phonemes of the particulardata records.

It is anticipated that AVR speech-to-text translation for the 36alphabetic characters of the English language, as well as theapproximately 41 distinct and unique phonemes of the English languagecan be easily performed utilizing state-of-the-art speect-to-texttranslation technology. One unique insight with this invention'smethodology is that less input may lead to more accurate results, inthat the search methodology utilizes a less complex speech-to-texttranslation method that results in a greater likelihood of retrieving adesired record due to the lessened chance of incorrectly translating amore complex word or sound.

The PRP that is formed from the use of AVR with the Alpha-Omega searchmethodology is comprised of a set of retrieved records consisting ofdissimilar spellings and phonemic sounds. This search methodologycreates very small, highly AVR-efficient PRPs with an automatic AVRprocedure to eliminate or select records based on phonemic sounds ofboth the first and last positional characters for one or more particularsearch terms.

In the context of utilizing the Alpha-Omega search methodology within anautomated directory assistance environment utilizing current AVRtechnology, advantages over prior art include: exact spelling or fullphonemic interpretation or translation is not required; consumption ofAVR resources is not as great; AVR call containment is improved whileAVR confidence scores increase in that matching is virtually 100% whenthe intervening characters or phonemes are excluded; and, finally, AVRspeed is increased.

In a second preferred embodiment of the invention, the novel searchsystem and method of the first preferred embodiment is performed withina directory assistance call center environment, preferably utilizing alive call handling agent and both speaker-independent andspeaker-dependent AVR technology.

Referring now to FIG. 7, a data retrieval system includes telephonedirectory assistance system having many features commonly encountered incurrent systems today. For instance, a computer has access to a memorydevice containing one or more telephone directory databases, or othertypes of information databases. As in typical current systems, a liveoperator has the ability to communicate with the computer via aconventional keyboard, and the computer has the ability to communicatewith the operator via information displayed on a video terminal screen.The operator communicates with a telephone via a conventional headsetthat includes a headphone mounted speaker and a microphone. Thetelephone communication device is connected to a calling customer via atelephony switch server. In this way, an operator can be within a callcenter operation or independently located and connected by telephonyswitch server via a public communications channel, such as the Internet,or a private or virtual private circuit, such as a dedicated line,integrated services digital network, or frame relay access device.Additionally, a voice communications interface, which includes a speechrecognition system, is positioned to allow the operator to communicatewith the computer via voice utterances into a headset microphone.Additionally, a direct voice communication interface is positionedbetween the calling customer and the computer. The direct voicecommunication interface includes an independent speech recognitionsystem capable of converting the identifying information spoken by thecalling customer into an independent searchable query that is availableto the means for searching the computer, as described below.

In one preferred aspect of this second preferred embodiment, a live callhandling agent utilizes a personal phoneme database to more efficientlyand more effectively formulate and execute search queries in accordancewith the system and method of the first preferred embodiment. Inaddition to the novel search methodologies and corresponding functionalcomponents necessary to effectuate said methodologies, as describedabove in the first preferred embodiment and all related aspects, thispreferred aspect of the second embodiment of the invention furtherincludes the use of a personal phoneme database for a live call handlingagent utilizing current speaker-dependent AVR technology. It isimportant to note that the use of personalized speech recognition for alive call handling agent has been awarded patent protection to theinventor(s) of this present invention, see U.S. Pat. No. 6,243,684 andU.S. Pat. No. 6,643,622 B2.

In accordance with this first preferred aspect of the second preferredembodiment, each individual live call handling agent utilizes a uniquepersonal phoneme database, where each unique personal phoneme databaseis comprised of one particular agent's unique alphanumeric phonemicutterances and AVR vocabulary. An agent's personal phoneme databaseincludes an individual agent's speaker-dependent phonemic sounds foralphanumeric characters and phonemic equivalents as well as anagent-specific vocabulary for common or frequent words, phrases, ordatabase commands. Agents may utilize individual speech vocabularies ofthe 36 alphabetic characters as well as simple words for operand commandstatements. The AVR translation of this very limited, but powerfulvocabulary and phonemes allows the agent to achieve 100% AVR confidencescores while retrieving records. This aspect of the invention furthercontemplates the use of a plurality of agent-specific databases insteadof ‘one database fits all’ of today's system. For instance, theagent-specific database may include agent-specific audiogramscorresponding to aspects of a database record, such a first and lastphonemes of one or more search fields.

In a second preferred aspect of this second preferred embodiment, acomposite progressive confidence score is utilized to more effectivelyand more efficiently formulate and execute search queries. Generallyspeaking, the progressive composite confidence score (PCCS) is derivedfrom a plurality of sources, including all components of AVR methods andsystems as well as from all components of the Alpha-Omega search methodand system. In particular, the PCCS is determined from a plurality ofinput components, including a query formulation component, an initialsearch query component, and a subsequent search query component. Themethod of utilizing the PCCS is comprised of the following steps:determining a first confidence score associated with formulating adesired query; determining a second confidence score associated withexecuting an initial search query; determining a third confidence scoreassociated with executing a subsequent search query; determining acombined confidence score from the first three confidence scores;selecting a record where the combined confidence score meets a minimumthreshold; and providing feedback to either a user of the system or toany computer program associated with the system.

With respect to the first determining step relating to formulating adesired search query, a first confidence score is determined in partfrom both the confidence of correctly interpreting a voiced utterancesearch input as well as from correctly interpreting a keyed searchinput. It is expected that a keyed input confidence score will virtuallyalways be 100%. With respect to the confidence score associated withcorrectly interpreting a voiced utterance search input, it is expectedthat this confidence score will be calculated from bothspeaker-independent AVR systems as well as speaker-dependent AVRsystems. This first confidence score results from an AVR system'sability to correctly interpret or translate a voiced utterance. Thisfirst determined confidence score includes a combined confidence scoreof speaker-independent and speaker-dependenttranslations/interpretations

The invention contemplates utilizing a confidence score associated withany currently-used speaker-independent scoring methods and systems.Ambiguity is a very large problem for AVR systems. Current AVR systemsmust request multiple attempts to achieve confidence scores above athreshold, determine proper translation for similar sounds (C or E).Today's AVR confidence scores do not utilize the Alpha-Omega format forsearch terms in today's directory assistance environment.

With respect to determining a confidence score relating to executing aninitial search query, this step of the method derives a confidence scoreassociated with matching the search query terms with records in thedatabase. This matching includes matching any one or more of thefollowing: first and last positional elements; any intervening elementbetween first and last positional elements; any virtual searchparameter. Moreover, determining an initial query confidence scoreincludes a confidence score associated with the number of matchingrecords relative to the initial query set of records as well asdetermining an initial query confidence score includes a confidencescore associated with a single record relative to the retrieved set ofrecords. Finally, determining an initial query confidence score includesa confidence score associated with the statistical frequency ofoccurrence of one or more elements in a search field, including one ormore of: first and last positional elements; an intervening elementbetween the first and last positional elements; any combination ofelements; whole words within a search field.

With respect to determining a confidence score relating to executing asubsequent search query, this step of the method derives a confidencescore associated with matching the subsequent search query terms withrecords in the database. This matching includes matching any one or moreof the following: first and last positional elements; any interveningelement between first and last positional elements; any virtual searchparameter. Moreover, determining a subsequent query confidence scoreincludes a confidence score associated with the number of matchingrecords relative to the subsequent query set of records as well asdetermining a subsequent query confidence score that includes aconfidence score associated with a single record relative to theretrieved set of records. Finally, determining a subsequent queryconfidence score includes a confidence score associated with thestatistical frequency of occurrence of one or more elements in a searchfield, including one or more of: first and last positional elements; anintervening element between the first and last positional elements; anycombination of elements; whole words within a search field.

With respect to determining a combined confidence score, the combinedconfidence score is derived from the first determining confidence scoreand one of or both of the second determining confidence score and thethird determining confidence score.

With respect to selecting a record where the combined confidence scoremeets a minimum threshold value. The method of the PCCS is capable ofutilizing virtual thresholds of flexible confidence scores calculatedfor each PLP and for individual listings or records. The current fixedconfidence scores may not be appropriate for all AVR transactions.

With respect to providing feedback, this method is capable of providingfeedback on any single confidence score or any combination of confidencescores. Moreover, the method is capable of providing feedback to a userof the system as well as to a computer program associated with thesystem. Finally, the method is capable of providing feedback at any timethroughout a search. The feedback may consist of statistical data.

A novel aspect of the progressive composite confidence scores is thecombining of multiple AVR factors with multiple database characteristicsand statistical calculations for initial, subsequent, and final PLPs toproduce a composite confidence score with relative and variable weightsassigned to various positional elements to identify the Most LikelyRequested listing or record. Moreover, multiple, progressive AVRaudiogram match attempts for PLPs with a smaller number of most likelyrequested listings will achieve higher AVR confidence scores and percentof calls contained within the AVR system.

In a third preferred aspect of this second preferred embodiment, asystem and method that utilizes an integrated system optimizer and anintelligent query system to dynamically assist a call handling agent orautomated attendant is utilized to formulate and execute more effectiveand more efficient search strategies in accordance with the firstpreferred embodiment of this invention.

The Integrated System Optimizer (ISO) is a supervision and monitoringsystem that is electronically interconnected with the various automaticvoice recognition, automated attendant store and forward, agentworkstation, database retrieval and automatic call distributor (ACD)systems currently found in today's current directory assistance callhandling environments. The ISO compares individual record positionalelements and attributes to determine the specific positional elementinformation that will disambiguate, eliminate the most records oridentify the desired record. In particular, the ISO is capable ofdetermining the size of the potential record pool for each of thesubsequent positional element data points.

The ISO receives search progress and status for each database query andthe ISO selects the query from a list of appropriate queries for missingresponses requiring clarification or AVR confidence scores below athreshold.

The data provided includes but is not limited to AVR confidence scoresfor each positional element; keyed input for each positional element;number of database records identified; potential record pools formed bypositional element provided input, probability of search query matchingindividual records.

The ISO formulates an IQS for the automated attendant system or the callcenter agent. Moreover, the ISO communicates the specific query to theIQS automated attendant store and forward system. Additionally, the ISOcommunicates search status to agent workstations.

The Intelligent Query System (IQS) formulates a query for an automatedattendant decision tree to elicit a customer response to providespecific positional element data or information that will disambiguateor identify records in a potential record pool. Moreover, the IQS iscapable of formulating a customer or agent response to a specificquestion to obtain data for missing (void) data or to ‘disambiguate’ agroup of records. The following is an example of how the ISO and the IQSwork together to formulate the most effective search query. If the ISOdetermines from a customer input query utilizing a prior art automatedattendant decision tree that a PRP will be comprised of 3 listings, theISO will compare the 3 listings. The ISO will next determine that thereare 3 different Omega characters for the street name, 3 different streettypes, and 3 different first names. The IQS query for the customer inthe automated attendant decision tree would be—

-   -   “Do you have the spelling of the last letter in the street        name?”    -   “Is this address a street, road, etc.”    -   “Do you have the spelling of the first and last letters of the        first name?”

The ISO and IQS use today's AVR platforms, database retrieval systems,‘decision-tree’ platforms. For example, IQS would simply add an AOprompt. ISO is located in the database retrieval platform but formulatesthe IQS prompt based on status of database search algorithm; astatistical module in the database platform combines AVR confidencescores with statistical AOSA and ASOF database data, etc.

The IQS with screening agents ‘added’ to the ‘decision tree’ willcontain calls within the AVR system.

The utilization of the personal phoneme database, the progressivecomposite confidence score, the ISO and the IQS within the presentinvention's search methodology can be demonstrated using the followingexample. A calling customer is connected to an automated attendantsystem as is currently utilized in present systems. The customer isprompted for a directory assistance inquiry, and the customer provides avoiced input for the listing “C. Smith” on “Maple Avenue.” The AVR forthe automated attendant system calculates the confidence score for thefirst name, in this example, the letter “C” to be less than 50%. Thislow confidence score is due in part to current speaker-independent AVRsystems' difficulties in interpreting certain letters and sounds, suchas “C” from “E.”

The ISO of the present system recognizes that the confidence score ofthe call in queue is below a minimum threshold and adds a screeningagent to the automated attendant system. The agent recognizes that thecustomer's request is for “C”. The agent utters “C” and the agent'spersonal phoneme database easily interprets the agent voiced-utterancewith over 99% confidence. The ISO then determines that the PRP of thematching records in the database is 1,200. The ISO then adds a screeningagent to the customer in the automated attendant queue with a requestfor a street name. The agent monitors the customer's recorded responsefor the address and keys or voices “M . . . E” for Maple. The PRPresulting from this address query is 1. The ISO then instructs the callto be routed to the Audio Response Unit.

In a fourth preferred aspect of this embodiment, agent operand commandsare utilized to direct a specific computer program instruction or step.These agent operand commands are composed of an action, a linking, andan object word. The combination of action, linking and object commandwords allows for the possibility of formulating multiple database searchqueries. Action words include “search”, “expand”, “delete.” Linkingwords include Boolean operators, specifically including “And” “or”,“all”, “except”, “only”, “not.” Object words include an element of adatabase record field, including any positional element of the field;object includes virtual search parameters, including the virtual searchparameter categorizations of ‘common’ and ‘uncommon.’

A Boolean-type logic with ‘and-or-all-only’ coupled with and ‘action’step (search, delete, etc.) directed to an object such as a specificdirectory listing or record characteristic (very common, common, etc.)or record length provides an almost infinite number of agent commands todirect and control various systems in an AVR database retrieval system.For example, Delete All Common listing; Search Only Average Length, etc.will reduce PLPs by 90-99%. This will cause a dramatic increase in AVRefficiency and accuracy. This PRS Boolean type command vocabularyincludes, but is not limited to, expand, delete, add, search, or, only,numeric, common, uncommon, uncommon-common, other, title, all, oralphanumeric character and any combination of two or more PRSBoolean-type words that may or may not be associated with various workstation keyed actions.

Examples includes—

-   -   “Search all street types Point”. The utterances would be search,        all, Point. (The Search Parameter key would direct the search        algorithm to the Locality field.)    -   “Search ‘I’ and ‘E’ Omega primary. The primary search Primary        key would instruct the search algorithm to search the database        to formulate a PRP with all records ending in ‘I or E’, etc.

The agent's operand command vocabulary may consist of single wordinstructions or sequential linked commands for more complex Boolean typeinstructions. A single command, ‘Expand,’ may direct the system tosearch the next geographic area or a sequential multiple word voicedcommand such as “search I, E, or Y” associated with an activated primaryname parameter key would be instructions for searching the last or omegacharacter for common spelling variations of primary names. Agent commandvocabulary further includes categorizations such as Yellow Pageclassifications, etc.

In a preferred embodiment, agent operand commands are vocalized, but canbe keyed, or a combination If vocalized, commands can be executed usingpersonalized, speaker-dependent (PSR) phonemic or whole-word utterances.

In a fifth preferred aspect of this invention, an integrated agentworkstation is utilized to more effectively and efficiently formulateand execute search queries using the Alpha-Omega search methodology ofthe first preferred embodiment. The understanding of the insights intothe Alpha-Omega search method and unique database characteristics leadto the fundamental need for an integration of the agent's work tool, theagent's workstation, with database architecture, search algorithms,search parameters and AVR technology.

The present invention utilizes dedicated keys that are directlyassociated with the ASOF database to effectuate this integration. It isenvisioned that the agent's workstation console will incorporate uniqueand separate multiple function keys associated with each searchparameter. It is also envisioned that an speaker-dependent AVR or PSRkey will be utilized to activate agent voiced operand commands tointegrate search algorithms and the database retrieval system. This PSRkey is included in order to inform the AVR and database retrievalsystems that a specific agent will vocalize a specific command with anoperand statement composed of words or phrases from the operandvocabulary. Moreover, the invention presupposes that certain commandscan be associated with dedicated workstation keys. These dedicatedworkstation keys are associated/linked with the revised database,including specific links to record fields, positional elements of recordfields, and with virtual search parameters

The agent workstation is capable of receiving ISO data that displayssearch status, composite progressive confidence scores, potential recordpool size, VSP and other data. In particular, the integrated agentworkstation is capable of receiving ISO data derived from ACD queuestatistics for average seconds of delay for individual calls andcustomer has previously abandoned call while in queue. Additionally, theintegrated workstation is capable of receiving ISO data relating to thenumber of records in the potential record pool. The integratedworkstation also is capable of receiving from the ISO suggested agentactions and their impact on reducing the number of records in the PRP.Finally, the integrated workstations receive the specific record orrecords and their individual composite and progressive confidencescores.

Given the foregoing discussion, it is helpful to summarize a typical,preferred call flow in a directory assistance environment utilizing theinvention's novel features. Attachment 2 summarizes the preferred callflow. Generally speaking, calls originate with a calling customer. Oncethe caller originates a call, the call is placed in queue and typicallyfirst serviced by a prior art automated attendant service. In the eventthe automated attendant service is not able to perform the caller'srequest, or in the event that a particular confidence threshold has notbeen achieved, the ISO function of the present invention monitors thecall progress, analyzes the positional elements of the caller's request,compares the positional elements with positional elements of thedatabase to determine potential record pool, and then either routes thecall to an automated response unit or adds a screening agent to utilizethe personal phoneme database to complete the search query. The ISOfurther communicates with the IQS to formulate more effective searchquery. The screening agent may be further utilized to complete thisquery, or, in the event a screening agent is not utilized, the ISO maydirect the IQS to query the calling customer utilizing the automatedattendant service while still keeping the calling customer in queue.

Attachment 2 is a study that demonstrates the effectiveness of theinvention's search methodology. The study includes all directorylistings for residents in Indianapolis whose last name begins with thealpha element “A.” In this study, the first and last positional elementquery “A . . . G” is queried on the Indianapolis directory assistancedatabase:

-   1. This initial query creates a PRP of 340 records, with 14 very    different PRPs based on spelling or phonemic sounds.-   2. An AO first and last positional element and any uncommon first    name or street name will create a PRP of 1 or a very small PRP of 2    or 3.-   3. ISO and IQS would identify an Omega for a first name or street    name in order to disambiguate-   4. Screening agents would provide speaker dependent confidence    scores to disambiguate “C” vs. “E”, etc.-   5. VSPs for “short” would separate Alig (4 characters) into a PRP    of 13. Any intervening positional character for street name would    create a PRP of 1.-   6. ‘Armstrong’ would be classified as a “long” VSP. Eliminating or    deleting Armstrong in this PRP would eliminate 294 of the 297.-   7. An ISO/ASOF database ‘cache’ statistical confidence score would    route this call to the ARU.-   8. Agent Operand commands for the non-positional ‘K’ would help find    a polish name Adonski or Adonske

Attachment 3 is a table of Indianapolis street names with percentagefrequency identified respectively for the first and last numericcharacters for each letter of the alphabet and numerical designations,respectively. By comparing the Alpha and Omega position characterspercentage frequency, one can easily see that the last positionalcharacter can be very powerful in finding a particular requesteddatabase record.

Attachment 4 is a table of reverse last Omega frequency in relation tothe Indianapolis telephone directory. This helps to show that in someinstances eliminating the subsets of the database by knowing thefrequency of occurrence of certain letters at certain positionalcharacters can be a shortcut in arriving at a requested database record.

Attachment 5 is a comparison of Alpha Omega frequency relationships forresidential listing s in the primary named search field for San Diegoand Indianapolis. This table helps illustrate that one Alpha-Omegaletter combination may be uncommon in one database, it may be extremelycommon in another. This factor can be exploited into the virtual searchparameters for different databases.

Attachment 6 is a researched example showing several search strategies,for finding a requested database record for Don Homback, on Fathom Crestin the Indianapolis phonebook database.

Attachment 7 is an example showing a typical search strategy and resultsand a search strategy according to the present disclosure on the right.Column labeled “KS” identifies key strokes in the order of entry.

Attachment 8 is a directory assistance worksheet example and severaldifferent searches and results for a requested database record for JamesWilliamson on Candlestick Way. The first search strategy is oneaccording to the prior art, whereas the second two search strategiesillustrate different aspects of the present disclosure.

Attachment 9 shows a more detailed explanation regarding searches forJames Williamson on Candlestick Way in the Indianapolis telephonedirectory database.

Attachment 10 is also an additional details regarding searches fordatabase record for a James Williamson on Candlestick Way in theIndianapolis telephone directory database.

Attachment 11 is a comparison of “A” residential listings. The San Diegoand Indianapolis telephone directory databases, respectively.

Attachment 12 is a comparison of the rarity of occurrence of differentstreet designations in the Indianapolis telephone directory database.This information can be useful in either eliminating or finding databaserecords, especially those having an uncommon designation, such ascircle, trail or lane.

Attachment 13 is a listing of data relating to database records of theIndianapolis telephone directory in relation to “C” streets. Therelative frequencies of occurrence can be exploited by a knowledgabledirectory assistance agent to find certain database records.

Attachment 14 shows some example virtual search parameters according tothe present disclosure.

Attachment 15 is a listing of frequency of occurrence of primary namesbeginning with “A” or “B” in the Indianapolis residential telephonedirectory database by the number of characters appearing in that searchfield.

Attachment 16 is a table of search parameter effectiveness that helpsillustrate how different search strategies can more efficiently arriveat a requested database record from within a database containing manyrecords.

Attachment 17 is a comparison of a typical forward fill strategysearching for “Alexander” in the Indianapolis telephone directory versesan Alpha Omega fill strategy that much more rapidly arrives at arequested database record.

Attachment 18 shows an example search results in the Indianapolistelephone directory where the Alpha and Omega search characters are Sand C. This table also shows how further searching within this potentiallisting pool can be accomplished by differentiating among the retrievedrecords to arrive at a single requested database record.

ATTACHMENT 1 MESA Sequence

-   1. Customer originating call-   2. ACD server—incoming trunk-   3. AVR CMS activated-   4. IQS customer queue-   5. MESA ISO call supervision (orig to term)-   6. AVR speaker independent and dependent system-   7. IQS announcement, response, recording-   8. AVR virtual confidence score-   9. IQS screening agent (silent or announced)-   10. MESA database actions    -   A<O formatted database    -   A<O street index    -   Personal phoneme database    -   A<O search algorithms    -   Search parameters    -   Virtual search parameters    -   Agent operand commands    -   Dynamic potential listing pool    -   Most effective search sequence/algorithm    -   Agent workstation status    -   Agent recommended action steps-   11. IQS call routing ARU-   12. IQS store and forward—call completion agent

Attachment 2

Indianapolis Res ‘A” A < O  A . . . g  Total 341 Listings for 14 PrimaryNames ‘ing’ < O Trigram ‘ong’ < O Trigram All Other Alberding 2 Adjong 1Agag 2 Alerding 19 Aleong 2 Alang 1 Alsing 1 Amlong 1 Alig 13 Ameling 1Armstrong 294 Amlung 1 Appling 1 297 Arkenberg 1 24 19 Total A-G = 340Conclusions:

-   -   1) A - - - g=86.4% probability Armstrong    -   2) A - - - g=95.9% probability Armstrong, Alerding or Alig    -   3) A - - - g=PLP of 340        -   Delete common A - - - g creates PLP of 14 (Delete Armstrong)    -   4) ISO communicates to customer and/or agent for A<O for        secondary or address or street type

-   1) Armstrong, Orville=PLP of 1

-   2) Aleong, D=PLP of 1

-   3) Alig, R, Mill Point=PLP of 1

-   4) Arkenberg, Washington St.=PLP of 1    Conclusion:    -   1) A<O plus Forename, Address, or Street Type creates PLP of 1,        even for common A<O.    -   2) A<O ISO will provide required data for ‘nested’ A<O SPPSC,*

-   3) Phoneme is AVR friendly

-   4) If inquiry is uncommon, PLP is reduced by 95%

-   5) If inquiry is very uncommon PLP is reduced by 99.7% (!)

Although the present invention has been illustrated in the context ofdelivering directory assistance services, the invention finds potentialapplication in a wide variety of other arenas. For instance, manycorporations and organizations maintain databases that are utilized byboth internal and/or external customers to retrieve database records.Thus, a customer according to the present invention could be an internalcorporate customer that contacts a database manager, who then performs asearch according to the present invention. In addition, other aspects ofthe disclosure could find potential application in non-service deliverysystems. For instance, Alpha Omega Search Algorithm strategy could besuccessfully implemented in cell phones to help facilitate retrieval offriend or relatives phone number from a personal database stored on thecell phone, or, the Alpha Omega Search strategy could be employed indigital video recorder systems to help a customer find a certain programin a television programming database so that the same can be recorded.Thus, the present invention is not limited to directory assistance orany provision of services that require a data retrieval from anelectronic database. Many other applications of the present inventionwill occur to those with ordinary skill in the art based upon the abovedescription, the attached drawings and the claims below.

ATTACHMENT 3

Indianapolis Street Names - Alpha < Omega (Percent Frequency) % A O A3.1 5.3 B 8.8 — C 9.65 .7 D 3.5 8.3 E 3.2 15.0 F 2.9 .3 G 4.0 2.7 H 3.92.0 I 1.1 .3 J .9 — K 2.9 2.7 L 4.9 5.6 M 7.4 1.0 O 2.0 2.7 P 5.3 1.3 Q.4 — R 5.4 9.0 S 9.6 7.3 T 3.8 6.3 U .2 .3 V 1.3 — W 6.7 2.0 X — .7 Y .310.3 Z — .3 Numerical 6.4

ATTACHMENT 4

REVERSE LAST OMEGA FREQUENCY (Given Name - Residence) % A 1.82 B — C .36D 2.73 E 10.02 F .73 G 1.64 H 3.46 I 1.46 J — K 4.19 L 6.19 M .73 N19.14 O 2.37 P 1.64 Q — R 13.84 S 12.93 T 5.46 U .18 V — W .91 X .55 Y7.83 Z 1.82Conclusion -1) <Omega search for common alpha reduces average potential listings by89.2%.2) Reverse last alpha search for uncommon alpha reduces averagepotential listings by 98.7%.

ATTACHMENT 5

ALPHA < OMEGA FREQUENCY RELATIONSHIP (Residential ‘A’ Listings - PrimaryName) San Diego Tot Res Listings = 521,525 Indianapolis = 452,600Listings Total Res ‘A’ = 4.13% 14212 3.14% 21539 1. A - 11.2% a 2.6% 370 a b .5 b .1  14 c .3 c — — d 3.6 d 4.0  568* e 6.9 e 5.6  796* f .4f .5  71 g 1.4 g 1.8  256 h 1.3 h 1.9  270 i 4.0 i 2.3  327 j — j — — k.4 k 1.0  142 l 2.9 l 2.8  398 m .9 m 1.3  185 n 29.6 n 32.0 4547 o 9.3o 2.3  327 p .2 p .4  57 q .2 q .2  28 r 10.7 r 9.0  1279* s 7.2 s 21.9 3112* t 3.5 t 4.4  625 u .7 u .4  57 v .3 v .1  14 w .2 w .4   57* x .2x .1  14 y 3.0 y 4.2  597* z 4.1 z .7  99 VCOM A-N 26.6/ 62.9% = A-N32.0/ A-A 11.2/ V Com A-S 21.9/ A-R 10.7/ % Common A-R 9.0/ 27.8% VUncom! Very A-b .1/14 A-v .1/57 Uncommon = A-f .5/71 A-w .4/57 A-p .4/57A-x .1/14 A-q .2/28 A-z .7/99 A-u .4/57 A-J — A-C —

ATTACHMENT 6

Residential Search Algorithm Comparison AOSA Example - Don Hornback,Fathom Crest Total Indianapolis Residence listings 452,000    PrimarySecondary/Sur Address 1) ‘H’ 36,400    or 769  Don 89 2) HOR-K 42 2) ‘H’35,400    or 769  K Don  4 3) HOR-K Don Fathom Crest  1 4) H----K Don 825) H----K Don F  2* H----K Don F-M  11) Hornback, Don 8928 Fathom Crest Indianapolis2) Huck, Donna 10890 Florida Rd. Fortville*The Huck (Fortville) might not be retrieved, if not, H K, D, F wouldretrieve Don's listing

ATTACHMENT 7 EXAMPLE

KS (1) KS (2) 1 W 30,000 1 W   30,000 2 I 2 Y    1,981 3 L  6,700 3 J    257 4 J   879 4 P      12** 5 Pine    41* 5 A    (1)****Manual search by call completion agent**No common (Wilson, Williams or Williamson) in small PLP formed byW----Y High probability ARU would automatically Contain without anyagent Assistance.***Note - Customer very likely to furnish A < O data prompting by IQS.1) Common trigram for common name Williams, J, Pine St.2) Common trigram for uncommon name Willoughby, J, Priscilla St.

ATTACHMENT 8 DA Worksheet—Example

Indianapolis Residence ‘W’ <O Most Efficient Search Sequence (<OMESS) DARequest Williamson, James, Candlewick Way PLP 1) TRIGRAMM Primary W30,000 WTL 6,700 Forename J 637 Address C 61 2) A < O W 30,000 WIL - N3,779 J 360 C 34 K <2 ‘Way’ 1 3) A < O with Mess ‘Way”* 1290 ‘C’ 122 ‘K’5 ‘W’ 1*One agent operand command plus 3 keystrokes

ATTACHMENT 9 DA Worksheet—Example

Indianapolis Residence ‘W’ Primary WIL --- N (Common) Secondary   A to Zforenames Forenames Common Uncommon A-B-C-D-E-G-J-K-L-M-P-R-S-TF-H-I-N-O-Q-U-V-W-X-Y-Z AVG PLP = 241 AVG PLP = 33.4 ‘J’ most common PLP= 518 Uncommon ‘1’ = 11 Data ‘WIL’ listings = 6,700 204 trigrams MostCommon - Williams, Wilson, Williamson Example WIL---A Only 4 names Allon different street 2 uncommon street types - LN & CT ISO Example 1) WIL--- A, any character   PLP of 1 or NF ARU 2) WIL --- A, street type PLPof 1 ARU 3) WIL --- A, PLP of 4 ARU

ATTACHMENT 10 DA Worksheet—Example Indianapolis Residence ‘W’

ATTACHMENT 11 Residence ‘A’ Listings

MOST FREQUENT TRIGRAM Residence ‘A’ Listings San Diego Indianapolis ByName By Trigram By Name By Trigram Anderson 1200 And 1800 1370 And 1900Allen 690 All 1000 1400 All 1766 Adams 550 Ada 800 870 Ada 1000 Alvarez460 Alv 580 60 Alv 180 Alexander 250 Ale 450 481 Ale 560 (Only 35 otherPrimary names) Alibegovic Arumugaswami Afsharkharegh Abdemrazzaq AlcockAldenderfer Aguilar Amick Andonov Alltop Alvarez  400 out of 580 907pages × 115 × 5 = 521,525 452,600 ‘A’ 37.5 × 575 = 21562/4.13%14212/3.14% Analysis A-Z = 883 A-Z = 99 Alvarez = 52.1% Alvarez = 60.6%prob SD ‘Z’ eliminates 95.9% Indpls ‘Z’ eliminates 99.3% of listings Sd< O for St eliminates 96% <O for St eliminates 96% Uncommon street typeUncommon street type <99% eliminates 99% Conclusion SD <O a, e, n, r, sIndpls <O n, s, r, e, t, y Requires 2 ‘VSP’ Needs + 2 ‘VSP’

ATTACHMENT 12 Street Designation Study

Common (5% or more Frequency)

-   -   Drive    -   Street    -   Numeric    -   Road    -   Court    -   Avenue        Uncommon (1% or less Frequency)    -   Point    -   Circle    -   Blvd    -   Crossing    -   Knoll    -   Trail    -   Walk    -   Cove    -   Way    -   Place    -   Ridge    -   Building    -   Run    -   Terrace    -   Bluff    -   Lane        Conclusion    -   1) PSR street designation a powerful search parameter to        identify or exclude listings. Approximately 90 to 99% of        potential listings may be excluded by this one parameters.        -   Example: ‘Delete all common street designations’            -   ‘Include Run only’ or ‘Search Run only’    -   2) Less than 50 street designations used in Indianapolis        Residence Directory. (!)

ATTACHMENT 13 Indianapolis ‘C’ Street Directory ‘C’ Streets=9.5%

-   1. Total Res ‘C’ Listings=33,200 or 7.33% of Res    -   Total Bus ‘C’ Listings=13,400 9.97 of Bus        -   Total ‘C’ Listings=46,600    -   Total Indianapolis Res=452,600 Bus=134,430 Total        Indianapolis=587,030-   2. Total street names ‘C’=866 Primary Name    Total street names with multiple key words=184 or 21.2%    -   Total ‘C’ common street names A<O=        -   Common<O=E, N, Y, L, R, D=68.3 AVG=11.4%        -   Uncommon <O=All other Alpha AVG=1.6%    -   Total ‘C’ streets for primary name plus street type=1.111    -   Total percent ‘C’ streets of total Indianapolis=9.5%-   3. Streets Per Col m=98.3    -   Total Indianapolis streets=11,698 (Cols 119×98.3)-   4. Average Res listings per ‘C’ street=407    -   Average Total listings per ‘C’ street=528

5. Most common street type for ‘C’ streets Common Uncommon Very UncommonDrive 330 29.7 Ave 48 4.3 Run 1 to 3 1 to 3% Court 243 21.9 Way 48 4.3Landing ″ Lane 120 10.8 70.5 Point 26 2.3 Mall ″ Circle 90 8.1 Blvd 171.5 Row ″ Road 68 6.1 Place 13 1.2 Walk ″ Street 66 5.9 Cove 6 .5 Square″ 836 75.2% Pkwy 6 .5 Trail ″ 164 14.8% Terrace ″ Bypass ″ Crossing ″Crescent ″ Trace ″ Ridge ″ 111 10.0%

-   7. Only 27 ‘C’ street types    -   6 common streets=75.2% (DR, CT, LN, CIR, RD, ST)    -   21 uncommon streets=24.8%    -   16 very uncommon streets=11%-   8. Listings per street type vary by type of street    -   Road, Street, Avenue, Boulevard and numeric streets have higher        than average number of listings    -   Circle, Way, Point, Cove, Run, Row, Walk, Trail, Terrace, Trace,        etc. have much fewer listings

ATTACHMENT 14

Virtual Search Parameters (VSP)

A subjective or objective characteristic that distinguishes a databaserecord from similar records. The individual record subjective orobjective characteristic may be fixed and predetermined based ondatabase ASOF variables and/or dynamically classified and assigned basedon the relative characteristic of smaller subsets of records (PLP's)identified by a search algorithm.

The following is a partial list of novel search parameter features thatserve to distinguish individual records from a group of ambiguousrecords—

-   -   Length (number of search parameter characters)    -   Length (short, average, long)    -   Language origin (Greek, Hispanic, Arabic, etc.)    -   Language (English, non-English)    -   Search parameter words (single, multiple, etc.)    -   Frequency (very common, common, uncommon, very uncommon, etc.)    -   Confidence Scores (negative, unsure, probable, positive)    -   Location (city, suburban, rural, etc.)    -   Geographic (N-E-S-W, etc.)    -   Spelling/sound complexity (difficult or multiple phonemes)    -   Business type (Yellow Page classification, professional titles,        etc.)    -   Purpose or use (fax, emergency, toll free, main, sales, etc.)

Any combination of a 3 parameter 5 character search plus only 1 VSP willnormally create a PLP without ambiguity for most U.S. localitydirectories.

ATTACHMENT 15

INDIANAPOLIS RESIDENCE A < 0 Primary Name ‘A’ & ‘B’ (Length) NumberCharacters ‘A’ ‘B’    3 .7% .7%    4 short  4.4 9.9    5 25.4 24.9   <6} Avg 23.4 24.2   >7 } 13.9 22.1     8 19.0 9.2    9 10.2 4.9   10 long 1.5 1.9   11  1.5 1.9   12  .3 100.0  100.0  Operand Command Delete +/−6 −53.9 PLP −59.7 PLP Search 4 only −95.6 −90.1 Search Long only −97.0−95.9 Search Short only −94.9 −85.3 Search Avg only −62.7 −53.7 Delete+/− Avg −67.8 −81.8Conclusion:

ASVP based on character length effectively eliminates 50 to 97% oflistings from the initial PLP. This VSP will reduce utilization of AVRsystem resources and increase the accuracy and effectiveness of theAVR—database retrieval system.

ATTACHMENT 16

Search Parameter Effectiveness % Reduction 1) Total 180,000,000−162,000,000 18,000,000 90 Bus - Gov - Res 16,200,000 1,800,000 90 State1,620,000 180,000 90 City 162,000 18,000 90 A Primary 16,000 2,000 90ABC Primary Trigram 1,800 200 90 A Secondary 180 20 90 Street @ 90% 7steps = 20 2) Total 180,000,000 −171,000,000 9,000,000 95 8,550,000450,000 95 427,500 22,500 95 21,375 1,125 95 1,069 56 95 53 3 95 @ 95% 6steps = 3 3) Total Listings 180,000,000 Total U.S. Business 27,000,00089 First Bus - Govt - Res Total State (IND) 742,000 97.5 State TotalCity (Indianapolis) 132,000 80 City Total ‘I’ 6,500 84 ‘I’ Section Total‘N’ 5,210 81 Trigram Total ‘IND’, IND ≦ S 4,206 98.7 Trigram + 1 StreetMeridian 2 95 Street AlphaConclusion

An additional search parameter is more effective the sooner it isutilized. For example, the initial search parameter, Business, is 90%effective by eliminating 160M Residence listings and limiting the searchto only the Business listings. If this initial search parameter includedthe Type of Business such as Legal, Finance, etc., the initialeffectiveness would be 99+%.

This more detailed search parameter for Indianapolis would produce aStep 5 search listing pool of only 5 listings vs. 3,000 with existingsearch techniques and algorithms. This enables PSR agent assisted IVR toselect from 5 listings vs. 3,000. A manual selection is also moreproductive (lower AWT) with a listing pool that is 99.8% smaller.

Conclusion:

-   -   1) ALOSA identifies the 1 correct listing witch search of        primary and address field plus normal surname single keyed        letter.    -   2) ALOSA for primary field and normal single key for both        surname and address reduces to only 2 listings.    -   3) The geographic designation (Fortville) will eliminate the        second listing for Huck. ALOSA with single character for surname        and address is very effective. Four keystrokes and/or agent        operand command more effective than today's trigram, plus        surname character. Listing pool with trigram contains 89        potential listings versus 1 or at most 2 with AOS!

Bottom Line—Use of AO (last alpha) in combination with normal trigramsearch algorithm reduced listing pool from 89 to 4. This is equivalentof another 95% search parameter. It is also an early use of an effectivesearch parameter. This should increase voice recognition effectiveness.AVR system match attempts vs. 4 listings instead of 89. The listingswith AOSA also tend to be different. (Hornback vs. Huck) trigram searchalgorithms produce similar spelling and sounding names (Indiana,Indianapolis, Wilson, Williams, etc.)

ATTACHMENT 17

ALPHA < OMEGA FILL vs. FORWARD FILL (Indianapolis Residence ‘A’ -Alexander Potential Vocabulary PWP Names Eliminated PWP Step StepALEXANDER Pool Per Step Cum % Accuracy % Error Forward Fill 1)A---------x 1800 0 0 0 100.0 2) Al--------x 438 −1362 −1362 .2 99.8 3)Ale-------x 30 −408 −1770 3.3 96.7 4) Alex------x 14 −16 −1786 7.1 92.95) Alexa-----x 5 −9 −1975 20 80 6) Alexan---x 4 −1 −1996 25 75 7)Alexand--x 2 0 −1796 25 75 8) Alexande-x 2 −2 −1798 50 50 9) Alexander 1−1 −1799 100 0 A < O FILL 1) A-------- 1800 0 0 0 100 2) A-------r 122−1678 −1678 .8 99.2 3) Al------r 23 −99 −1777 4.3 95.7 4) Ale-----r 1−22 −1799 100.0 0

ATTACHMENT 18

1. A method of searching a database, comprising the steps of: inputtinga search query that includes an attribute that is not part of arequested database record, but the attribute being a characteristic ofthe requested database record; executing the search query; and selectingat least one database record that matches the search query.
 2. Themethod of claim 1 wherein the attribute includes a number ofalphanumeric characters in a search field of the requested databaserecord.
 3. The method of claim 1 wherein the attribute includes anindication of a number of words in a search field of the requesteddatabase record.
 4. The method of claim 1 wherein the attribute includesa number of syllables in a pronunciation of an entry in a search fieldof the requested database record.
 5. The method of claim 1 wherein thesearch field includes a name search field.
 6. The method of claim 1wherein the attribute includes an indication of whether a word in asearch field is of a non-English origin; and the search field is a namesearch field.
 7. The method of claim 1 wherein the search query includesan indicator of a relative size of a portion of the requested databaserecord.
 8. The method of claim 1 wherein the search query includes anindicator of relative pronunciation complexity.
 9. The method of claim 1wherein the attribute includes a relative rarity of a feature of therequested database record.
 10. The method of claim 1 wherein the searchquery is directed to an address search field.
 11. The method of claim 1wherein the attribute is a relative characterization of the requesteddatabase record relative to at least one other database record; and theattribute is selected from a predetermined finite number of differentcharacterizations.
 12. The method of claim 11 wherein the attribute isrelative among a plurality of database records.
 13. The method of claim1 wherein the attribute is an objective attribute.
 14. The method ofclaim 1 wherein the search query includes non-sequential first and lastelements of a search field of a requested database record, and thesearch query omits a plurality of elements of the search fieldpositioned between the first and last elements.
 15. The method of claim1 including a step of eliminating a portion of the selected databaserecords at least in part by identifying an aspect of a database recordthat is absent from the requested database record.
 16. The method ofclaim 1 wherein the inputting step includes a step of vocalizing atleast a portion of the search query to a voice recognition system.
 17. Amethod of providing a database record retrieval service, comprising thesteps of: establishing a communication link between a customer and aservice provider, and the communication link originating from thecustomer; receiving requested database record information from thecustomer; formulating a search query based on the requested databaserecord information that includes an attribute that is not part of therequested database record, but the attribute being a characteristic ofthe requested database record; executing the search query: selecting atleast one database record that matches the search query; and supplyingthe customer with information from the selected database record.
 18. Themethod of claim 17 wherein the formulating step includes the steps of:screening information supplied by the customer with a live screeningagent; and formulating the search query based upon at least one of,information from a speaker independent voice recognition system andinformation from the live screening agent.
 19. The method of claim 18wherein the information from the speaker independent voice recognitionoriginates from a customer utterance; and the information from the livescreening agent includes an utterance processed by a speaker dependentvoice recognition system associated with utterances specific to the livescreening agent.
 20. The method of claim 19 including a step ofdetermining a confidence score based upon a determined accuracy of aresult from the speaker independent voice recognition system; the searchquery includes a portion from the live screening agent if the confidencescore is below a first predetermined threshold, but includes onlyinformation from the speaker independent voice recognition system if theconfidence score is above a second predetermined threshold.
 21. Themethod of claim 17 wherein the selecting step includes selecting asubset of the database that includes a plurality of database recordsthat match the search query, which is a first search query; identifyingan aspect of each of the database records of the subset that is uniqueto the respective database records; formulating a second search query atleast in part by requesting additional information regarding the aspectfrom the customer; executing the second search query on the subset; andretrieving, from the subset, at least one database record that matchesthe second search query.
 22. The method of claim 21 wherein theadditional information is derived from an utterance from the customer tothe speaker independent voice recognition system; and the requestingstep includes a step of selecting one of a plurality of predeterminedquestions to the customer based upon the identified aspect.
 23. Themethod of claim 22 wherein the first search query includes portions of aname search field associated with the requested database record; and thesupplying step includes a step of providing a telephone number to thecustomer.
 24. The method of claim 23 wherein the second search queryincludes a portion of an address search field.